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Kernel Based Algorithms for Mining Huge Data Sets : Supervised, Semi-supervised, and Unsupervised Learning
"Kernel Based Algorithms for Mining Huge Data Sets" is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction (feature elimination) and shows the similarities and differences between the two most popular unsupervised techniques, the principal component analysis (PCA) and the independent component analysis (ICA).
Complex Medical Engineering
In the twenty-first century, applications in medicine and engineering must acquire greater safety and flexibility if they are to yield better products at higher efficiency. To this end, complex science and technology must be integrated in medicine and engineering. Complex medical engineering (CME) is a new field that merges medical science and technology, and includes biomedical robotics and biomechatronics, complex virtual technology in medicine, information and communication technology in medicine, complex technology in rehabilitation, cognitive neuroscience and technology, and complex bioinformatics. Experts from academia, industry, and government research laboratories who have pioneered CME ideas and technologies describe its concept and research approach and discuss related hardware and software, science and technology, and medicine and engineering. This book will be invaluable to scientists, researchers, and graduates in the emerging field of CME.
Bioremediation of Soils Contaminated with Aromatic Compounds
Environmental biotechnology, which was in its infancy in the early 80's, has evolved thanks to the revolution brought about by molecular biology. Multiple successes in the biological cleanup of civil and industrial wastewater and of hydrocarbon soil pollution, demonstrate the vast power of clean technologies. In addition, the buildup of information on the activities of microorganisms as catalysts in all sorts of natural, industrial and animal environments has flourished. There is a continuing realization of the critical role of microbial processes in biological, industrial and geological systems. Since environmental biotechnology has matured, it is ready to tackle bigger challenges: the scaling up of many bioremediation systems still in progress, the search for novel biocatalysts for industrial applications, the continuing effort against common human life-threatening processes such as antibiotic resistance, the accumulation of hormone-mimicking substances (endocrine disrupters), the deposition of air-borne pesticides in the environment and, the degradation of recalcitrant contaminants. These endeavors will help prevent the contamination of food chains, protect human life and allow for human activity and economic development that do not compromise environmental sustainabijity.
Bio-informatique moléculaire : Une approche algorithmique = Molecular bioinformatics : An algorithmic approach
Deals with genetic maps, from the problem of sequence comparison and alignment, including DNA chips and genomic rearrangement. It thus covers a wide variety of topics relating to algorithmic and combinatorial processing of questions arising from molecular bioinformatics and biotechnology.
Bioinformatics Using Computational Intelligence Paradigms
Bioinformatics as well as Computational Intelligence are undoubtedly remarkably fast growing fields of research and real-world applications with enormous potential for current and future developments. "Bioinformatics using Computational Intelligence Paradigms" contains recent theoretical approaches and guiding applications of biologically inspired information processing systems(Computational Intelligence) against the background of bioinformatics. This carefully edited monograph combines the latest results of Bioinformatics and Computational Intelligence and offers a promising cross-fertilisation and interdisciplinary work between these growing fields.
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment R. This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including: Importation and preprocessing of high-throughput data from microarray, proteomic, and flow cytometry platforms / Curation and delivery of biological metadata for use in statistical modeling and interpretation. / Statistical analysis of high-throughput data, including machine learning and visualization,modeling and visualization of graphs and networks. This book is a dynamic document. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.
Applied soft computing technologies : The challenge of complexity
This volume presents the proceedings of the 9th Online World Conference on Soft Computing in Industrial Applications (WSC9), September 20th - October 08th, 2004, held on the World Wide Web. It contains plenary lectures, original papers and tutorials presented during the conference. The book brings together outstanding research and developments in the field of soft computing (evolutionary computation, fuzzy logic, neural networks, and their fusion) and its applications in science and technology.
Applications of computational intelligence in biology : Current trends and open problems
The purpose of this book is to provide a medium for such an exchange of expertise and concerns. In order to achieve the goal, we have solicited cont- butions from both computational intelligence as well as biology researchers.
Analysis of phylogenetics and evolution with R
This book integrates a wide variety of data analysis methods into a single and flexible interface: the R language. This open source language is available for a wide range of computer systems and has been adopted as a computational environment by many authors of statistical software. Adopting R as a main tool for phylogenetic analyses will ease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments.
Advances in Probabilistic Graphical Models
This carefully edited book brings together in one volume some of the most important topics of current research in probabilistic graphical modelling, learning from data and probabilistic inference. This includes topics such as the characterisation of conditional independence, the sensitivity of the underlying probability distribution of a Bayesian network to variation in its parameters, the learning of graphical models with latent variables and extensions to the influence diagram formalism. In addition, attention is given to important application fields of probabilistic graphical models, such as the control of vehicles, bioinformatics and medicine.
4th Kuala Lumpur International Conference on Biomedical Engineering 2008 ; BIOMED 2008 25–28 June 2008 Kuala Lumpur, Malaysia
The topics covered in the conference proceedings include: Artificial organs, bioengineering education, bionanotechnology, biosignal processing, bioinformatics, biomaterials, biomechanics, biomedical imaging, biomedical instrumentation, bioMEMS, clinical engineering, prosthetics.
